Semi-Supervised Histopathology Image Segmentation with Feature Diversified Collaborative Learning
Nguyen, Thanh-Huy Vu, Nguyen Lan Vi Nguyen, Hoang-Thien Dinh, Quang-Vinh Li, Xingjian Xu, Min
Nguyen, Thanh-Huy
Vu, Nguyen Lan Vi
Nguyen, Hoang-Thien
Dinh, Quang-Vinh
Li, Xingjian
Xu, Min
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Abstract
Histopathology image segmentation plays a critical role in advancing disease diagnosis, prognosis, and treatment planning. However, it presents significant challenges due to the complexity of tissue structures, staining variability, and low contrast between tissue classes. Semi-supervised learning, employed to mitigate annotation scarcity, introduces additional difficulties, such as managing noisy pseudo-labels while ensuring robust performance with limited supervision. Traditional collaborative training methods, commonly used in medical image segmentation, often face issues like model coupling—where models become overly dependent on each other, propagating similar errors or confirmation bias, where networks reinforce initial mistakes by relying on inaccurate pseudo-labels. Existing frameworks designed to tackle these challenges often suffer from complex pipelines and require extensive pre-training but fail to address the noise characteristics inherent in such datasets.
To balance the efficiency of traditional co-training methods with dual networks while enhancing segmentation accuracy on noisy histopathological data, we propose Feature Diversified Collaborative Learning (FDCL). Our work aims to design an effective feature diversification loss that encourages the feature representations of sub-networks to be distinct, ensuring they capture different information to exchange with each other, thereby avoiding suboptimal solutions or, even worse, falling into the coupling problem. We benchmark our method on two well-known histopathology datasets and achieve state-of-the-art results on the GlaS dataset with only 10% of the labeled data. Code is available at: https://github.com/vnlvi2k3/FDCL
Citation
T.-H. Nguyen et al., “Semi-Supervised Histopathology Image Segmentation with Feature Diversified Collaborative Learning,” 2025. [Online]. Available: www.aaai.org
Source
AAAI Bridge Program on AI for Medicine and Healthcare
Conference
Association for the Advancement of Artificial
Intelligence, 2025
Keywords
Subjects
Source
Association for the Advancement of Artificial
Intelligence, 2025
Publisher
PMLR